Biometric recognition apparatus and biometric recognition method

ABSTRACT

A biometric recognition apparatus and fingerprint feature extraction method can automatically optimize parameters used for extracting a feature template from a biometric image. The biometric recognition apparatus includes: a teacher data generation unit that generates a genuine pair and an imposter pair of first and second biometric images; a learning data generation unit that uses a plurality of different temporary parameters to extract feature templates from the first biometric image and the second biometric image; and an optimum solution determination unit that calculates a score separation degree on the temporary parameter basis based on a first score representing a similarity degree of a pair of the feature templates extracted from the genuine pair and a second score representing a similarity degree of a pair of the feature templates extracted from the imposter pair and determines the temporary parameter based on a level of the score separation degree.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a Continuation of U.S. application Ser. No.16/322,233, filed Jan. 31, 2019, which is a National Stage ofInternational Application No. PCT/JP2018/034075 filed Sep. 13, 2018,claiming priority based on Japanese Patent Application No. 2017-199167filed Oct. 13, 2017.

TECHNICAL FIELD

The present invention relates to a biometric recognition apparatus and abiometric recognition method, in particular, relates to a technology ofautomatically optimizing parameters used for extracting a featuretemplate from a biometric image.

BACKGROUND ART

Fingerprints or palm prints formed by ridges on skin of a finger or apalm have uniqueness and permanence in the life. Thus, fingerprints orpalm prints have been used in criminal investigation or the like as abiometric recognition scheme for identifying individuals.

Collected images such as tenprint fingerprints collected in advance forcriminal investigation, latent fingerprints collected at a crime scene,or the like are registered in advance in a database and read in criminalinvestigation, and the feature template thereof is extracted. In PatentLiterature 1, a noise reduction process or an enhancement process isperformed when a feature template is extracted from a collected image,and thereby the feature template of a fingerprint is clarified toimprove the verification accuracy of the fingerprint.

CITATION LIST Patent Literature

PTL 1: Japanese Patent Application Laid-Open No. 2007-048000

SUMMARY OF INVENTION Technical Problem

Parameters such as a combination of a noise reduction process and anenhancement process, a weighting, and the like used when a featuretemplate is extracted from a biometric image may have different optimumvalues depending on a collection method of a fingerprint (for example,quality of ink or a sheet in an ink scheme) or the like. Thus,parameters used for extracting a feature template from a biometric imageare required to be set suitably in accordance with an operation style ofa biometric recognition apparatus.

However, setting parameters used for extracting a feature template froma biometric image for each operation style requires time and labor, andin addition, the optimum parameters are often not known before trial anderror in the actual operation. In particular, since the operation stylemay often be changed during operation, biometric images collected invarious operation styles may be included in the database. Thus, there isa problem of difficulty in maintaining optimized parameters used forextracting a feature template from a biometric image.

Solution to Problem

According to one example aspect of the present invention, provided is abiometric recognition apparatus including: a teacher data generationunit that generates a genuine pair and an imposter pair of a firstbiometric image and a second biometric image; a learning data generationunit that uses a plurality of different temporary parameters to extractfeature templates from the first biometric image and the secondbiometric image; and an optimum solution determination unit thatcalculates a score separation degree on the temporary parameter basisbased on a first score representing a similarity degree of a pair of thefeature templates extracted from the genuine pair and a second scorerepresenting a similarity degree of a pair of the feature templatesextracted from the imposter pair and determines the temporary parameterbased on a level of the score separation degree.

According to another example aspect of the present invention, providedis a biometric recognition method used in a control calculation unit ofa biometric recognition apparatus, and the biometric recognition methodincludes: a teacher data generation step of generating a genuine pairand an imposter pair of a first biometric image and a second biometricimage; a learning data generation step of using a plurality of differenttemporary parameters to extract feature templates from the firstbiometric image and the second biometric image; and an optimum solutiondetermination step of calculating a score separation degree on thetemporary parameter basis based on a first score representing asimilarity degree of a pair of the feature templates extracted from thecorrect solution pair and a second score representing a similaritydegree of a pair of the feature templates extracted from the imposterpair and determining the temporary parameter based on a level of thescore separation degree.

Advantageous Effects of Invention

According to the present invention, a biometric recognition apparatusand a fingerprint feature extraction method that can automaticallyoptimize parameters used for extracting a feature template from abiometric image can be provided.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram schematically illustrating a configuration ofa biometric recognition apparatus according to a first exampleembodiment.

FIG. 2A is a diagram illustrating an example of a teacher datageneration process in the biometric recognition apparatus according tothe first example embodiment.

FIG. 2B is a diagram illustrating an example of a teacher datageneration process in the biometric recognition apparatus according tothe first example embodiment.

FIG. 3A is a diagram illustrating an example of a learning datageneration process in the biometric recognition apparatus according tothe first example embodiment.

FIG. 3B is a diagram illustrating an example of a learning datageneration process in the biometric recognition apparatus according tothe first example embodiment.

FIG. 4A is a first diagram illustrating an example of a scorecalculation process in the biometric recognition apparatus according tothe first example embodiment.

FIG. 4B is a first diagram illustrating an example of a scorecalculation process in the biometric recognition apparatus according tothe first example embodiment.

FIG. 5A is a second diagram illustrating an example of a scorecalculation process in the biometric recognition apparatus according tothe first example embodiment.

FIG. 5B is a second diagram illustrating an example of a scorecalculation process in the biometric recognition apparatus according tothe first example embodiment.

FIG. 6A is a third diagram illustrating an example of a scorecalculation process in the biometric recognition apparatus according tothe first example embodiment.

FIG. 6B is a third diagram illustrating an example of a scorecalculation process in the biometric recognition apparatus according tothe first example embodiment.

FIG. 7 is a diagram illustrating an example of an optimum solutiondetermination process in the biometric recognition apparatus accordingto the first example embodiment.

FIG. 8 is a diagram illustrating a flowchart of a biometric recognitionmethod according to the first example embodiment.

DESCRIPTION OF EMBODIMENTS

Preferred example embodiments of the present invention will be describedbelow by using drawings. Note that the present invention is not limitedto the following example embodiments and can be modified as appropriatewithin the scope not departing from the spirit thereof. Throughout thedrawings, components having the same or corresponding function arelabeled with the same references, and the description thereof may beomitted or simplified.

First Example Embodiment

FIG. 1 is a block diagram schematically illustrating a configuration ofa biometric recognition apparatus according to a first exampleembodiment. The biometric recognition apparatus of the present exampleembodiment has a control calculation unit 1 and a storage unit 2.

The control calculation unit 1 is a semiconductor IC having amicroprocessor and a memory used for executing a program stored in thestorage unit 2 and performing control and calculation in the biometricrecognition apparatus. The control calculation unit 1 has a biometricimage read unit 11, a teacher data generation unit 12, a learning datageneration unit 13, an optimum solution determination unit 14, and aparameter update unit 15.

The storage unit 2 is a storage device such as a flash memory, an HDD,or the like in which a program executed by the control calculation unit1 or data required for executing a program is stored. In the storageunit 2, biometric images such as a tenprint fingerprint collected inadvance for criminal investigation, a latent fingerprint collected at acrime scene, or the like have been registered as a database.

The biometric image read unit 11 reads a plurality of biometric imagesout of biometric images registered as a database in the storage unit 2in order to perform machine learning for automatically optimizingparameters used for feature template extraction. The automaticoptimization of parameters described below may be performed as a background process of a usual identification process or a batch processperformed at night or the like.

Note that, while a biometric image may be a collected image of atenprint fingerprint collected in advance for criminal investigation ora collected image of a latent fingerprint collected at a crime scene inthe following description, the present example embodiment is not limitedthereto. The biometric image may be a collected image of veins of afinger, an iris, or the like. Further, a biometric image may be an imageobtained by directly reading biometric information by using a camera orthe like or an image obtained by reading biometric information stampedon a sheet with ink or the like by using a scanner.

The teacher data generation unit 12 generates a pair of a firstbiometric image and a second biometric image used as teacher data in alearning data generation process described below. In the present exampleembodiment here, a collected image of a latent fingerprint is the firstbiometric image, and a collected image of a tenprint fingerprint is thesecond biometric image.

More specifically, the teacher data generation unit 12 selects a pair ofthe first biometric image and the second biometric image which aredetermined as being based on the same finger as a genuine pair out of aplurality of biometric images read from the biometric image read unit11. Further, the teacher data generation unit 12 selects a pair of thefirst biometric image and the second biometric image which are notdetermined as being based on the same finger as an imposter pair.

The teacher data generation unit 12 is not necessarily required toperform a dedicated process for generating teacher data but may utilizeone process in a usual identification operation as one process performedin the teacher data generation unit 12. For example, the teacher datageneration unit 12 may select, directly as a genuine pair, a pair of thefirst biometric image and the second biometric image identified inadvance as being based on the same finger by a latent print examiner orthe like who is a user of the biometric recognition apparatus.

The learning data generation unit 13 uses a plurality of differenttemporary parameters from the first biometric image and the secondbiometric image used for teacher data generated by the teacher datageneration unit 12 and generates a feature template used for learningdata. In the present example embodiment here, a group of feature points(end points or branch points of fingerprint ridges) extracted from acollected image of a latent fingerprint or a tenprint fingerprint is afeature template used for learning data.

Note that the term of parameter is used in a broad meaning in thepresent example embodiment. The parameter may include a combination anda weight of algorithms of a noise reduction process, an enhancementprocess, and other processes in image extraction.

The optimum solution determination unit 14 calculates a score separationdegree on a temporary parameter basis based on a first scorerepresenting a similarity degree in a pair of feature templatesextracted from a genuine pair and a second score representing asimilarity degree in a pair of feature templates extracted from animposter pair. The optimum solution determination unit 14 thendetermines the temporary parameter as the optimum solution based on thelevel of the score separation degree. The parameter update unit 15updates a parameter stored in the storage unit 2 with a temporaryparameter determined as the optimum solution.

FIG. 2A and FIG. 2B are diagrams illustrating an example of a teacherdata generation process in the biometric recognition apparatus accordingto the first example embodiment. The teacher data generation unit 12selects a pair of the first biometric image and the second biometricimage which are determined as being based on the same finger as agenuine pair out of a plurality of biometric images read from thebiometric image read unit 11. Further, the teacher data generation unit12 selects a pair of the first biometric image and the second biometricimage which are not determined as being based on the same finger as animposter pair.

Note that, while FIG. 2A illustrates a collected image of a latentfingerprint F and collected image of a tenprint fingerprint f generatedby the teacher data generation unit 12 by using the same figure, theactual collected image of the latent fingerprint F is less clear thanthe collected image of the tenprint fingerprint f. While an example of ageneration method of a genuine pair and an imposter pair performed bythe teacher data generation unit 12 will be described below, the presentexample embodiment is not limited to such an example.

The teacher data generation unit 12 generates, as teacher data, agenuine pair of a collected image of the latent fingerprint F and acollected image of the tenprint fingerprint f which have been identifiedas being based on the same finger by a latent print examiner or the likewho is the user of the biometric recognition apparatus. Further, theteacher data generation unit 12 generates, as teacher data, an imposterpair of a collected image of the latent fingerprint F and a collectedimage of the tenprint fingerprint f which have not been identified asbeing based on the same finger by a latent print examiner or the likewho is the user of the biometric recognition apparatus.

FIG. 2A illustrates a feature point group {M1, M2, . . . } of thecollected image of the latent fingerprint F that is the first biometricimage and a feature point group {m1, m2, . . . } of the collected imageof the tenprint fingerprint f that is the second biometric image forreference. While, in general, a latent print examiner selects a genuinepair and an imposter pair of the first biometric image and the secondbiometric image by referencing the feature point group {M1, M2, . . . }and the feature point group {m1, m2, . . . } as illustrated in FIG. 2A,the present example embodiment is not limited thereto.

FIG. 2B illustrates an example of a plurality of genuine pairs F1-f1,F2-f2, and F3-f3 generated by the teacher data generation unit 12. Inthis example, the genuine pair F1-f1 is a genuine pair of the collectedimage of the latent fingerprint F1 and the collected image of thetenprint fingerprint f1, the same applies to other genuine pairs F2-f2and F3-f3. Note that, while three genuine pairs are illustrated in FIG.2B, more genuine pairs may be generated in the actual teacher datageneration process.

FIG. 3A and FIG. 3B are diagrams illustrating an example of a learningdata generation process in a biometric recognition apparatus accordingto the first example embodiment. The learning data generation unit 13uses a plurality of different temporary parameters from the firstbiometric image and the second biometric image of a genuine pair usedfor teacher data generated by the teacher data generation unit 12 andgenerates a feature template used for learning data. In the presentexample embodiment here, a group of feature points (end points or branchpoints of fingerprint ridges) extracted from a collected image of alatent fingerprint or a tenprint fingerprint is a feature template usedfor learning data. FIG. 3B illustrates the first biometric image and thesecond biometric image of a genuine pair illustrated in FIG. 2B. Whileone example of a generation method of a feature template performed bythe learning data generation unit 13 will be described below, thepresent example embodiment is not limited to such an example.

As illustrated in FIG. 3A, the learning data generation unit 13 usestemporary parameters to extract a feature point group G{N1, N2, . . . }from the collected image of the latent fingerprint F. Further,similarly, the learning data generation unit 13 uses temporaryparameters to extract a feature point group g{n1, n2, . . . } from thecollected image of the tenprint fingerprint f. As an extraction methodof feature points, a known technology disclosed in Patent Literature 1may be used, for example.

FIG. 3B illustrates feature point groups G1, G2, and G3 and featurepoint groups g1, g2, and g3 generated by the learning data generationunit 13 together with the latent fingerprints F1, F2, and F3 and thetenprint fingerprints f1, f2, and f3 of genuine pairs generated by theteacher data generation unit 12. In this example, the feature pointgroup G1 is a group of feature points extracted from the collected imageof the latent fingerprint F1 that is the first biometric image, and thefeature point group g1 is a group of feature points extracted from thecollected image of the tenprint fingerprint f1 that is the secondbiometric image. The same applies to other feature point groups G2, G3,g2, and g3. Note that, while three genuine pairs of the first biometricimage and the second biometric image are illustrated in FIG. 3B, afeature template may be generated from more first biometric images andmore second biometric images in the actual learning data generationprocess.

FIG. 4A and FIG. 4B are first diagrams illustrating an example of ascore calculation process in the biometric recognition apparatusaccording to the first example embodiment. FIG. 4A illustrates featurepoint groups Ga1 to Ga3 extracted from the latent fingerprints F1 to F3,respectively, by the learning data generation unit 13 using a temporaryparameter Pa. Further, similarly, FIG. 4A illustrates feature pointgroups ga1 to ga3 extracted from the tenprint fingerprints f1 to f3,respectively, by the learning data generation unit 13 using a temporaryparameter pa.

As illustrated in FIG. 4A, the optimum solution determination unit 14calculates a score Saij representing a similarity degree of a pairGai-gaj of a feature point group for each combination of a feature pointgroup Gai (“i” is a natural number) extracted from the first biometricimage and a feature point group gaj (“j” is a natural number) extractedfrom the second biometric image.

At this time, the optimum solution determination unit 14 calculates notonly a score representing a similarity degree of a pair of featuretemplates extracted from a genuine pair (hereinafter, referred to as“first score”) but also a score representing a similarity degree of apair of feature templates extracted from an imposter pair (hereinafter,referred to as “second score”). FIG. 4B illustrates the first scoreSaij(i=j) of a pair of feature templates extracted from a genuine pairin the upper part and the second score Saij(i≠j) of a pair of featuretemplates extracted from an imposter pair in the lower part.

FIG. 5A and FIG. 5B are second diagrams illustrating an example of ascore calculation process in the biometric recognition apparatusaccording to the first example embodiment. FIG. 5A illustrates featurepoint groups Gb1 to Gb3 extracted from the latent fingerprints F1 to F3,respectively, by the learning data generation unit 13 using a temporaryparameter Pb. Further, similarly, FIG. 5A illustrates feature pointgroups gb1 to gb3 extracted from the tenprint fingerprints f1 to f3,respectively, by the learning data generation unit 13 using a temporaryparameter pb.

The optimum solution determination unit 14 calculates a score Sbijrepresenting a similarity degree of a pair Gbi-gbj of a feature pointgroup for each combination of a feature point group Gbi and a featurepoint group gbj as with FIG. 4B. FIG. 5B illustrates a score Sbijcalculated by the optimum solution determination unit 14.

FIG. 6A and FIG. 6B are third diagrams illustrating an example of ascore calculation process in the biometric recognition apparatusaccording to the first example embodiment. FIG. 6A illustrates featurepoint groups Gc1 to Gc3 extracted from the latent fingerprints F1 to F3,respectively, by the learning data generation unit 13 using a temporaryparameter Pc. Further, similarly, FIG. 6A illustrates feature pointgroups gb1 to gb3 extracted from the tenprint fingerprints f1 to f3,respectively, by the learning data generation unit 13 using a temporaryparameter pc.

The optimum solution determination unit 14 calculates a score Scijrepresenting a similarity degree of a pair Gci-gcj of a feature pointgroup for each combination of a feature point group Gci and a featurepoint group gcj as with FIG. 4B. FIG. 6B illustrates a score Scijcalculated by the optimum solution determination unit 14.

Next, the optimum solution determination unit 14 calculates a scorevalue representing a statistical separation degree between the firstscore representing a similarity degree of a pair of feature templatesextracted from a genuine pair and the second score representing asimilarity degree of a pair of feature templates extracted from animposter pair. While one example of a calculation method of a scoreseparation degree will be described below with reference to FIG. 7 , acalculation method of a score separation degree of the present exampleembodiment is not limited to such an example.

FIG. 7 is a diagram illustrating an example of an optimum solutiondetermination process in the biometric recognition apparatus accordingto the first example embodiment. The optimum solution determination unit14 calculates the score Sij representing the similarity degree betweenthe latent fingerprint Fi and a tenprint fingerprint fj by the followingEquation (1) by differentiating weights (A, B, C) of the temporaryparameter P. Here, values Saij, Sbij, and Scij denote scores illustratedin FIG. 4B, FIG. 5B, and FIG. 6B, and values A, B, and C denote weightsof scores Saij, Sbij, and Scij, respectively.Sij=A×Saij+B×Sbij+C×Scij+ . . .   (1)

Note that, while the score Sij is calculated by using three combinationsof weights of the temporary parameter P (1, 1, 1), (1, 2, 1), and (2,1, 1) in FIG. 7 , more combinations of weights of the temporaryparameter P (A, B, C) may be used in the actual optimum solutiondetermination process.

First, the optimum solution determination unit 14 calculates the firstscore Sij(i=j) representing the similarity degree of a pair of featuretemplates extracted from a genuine pair and the second score Sij(i≠j)representing a similarity degree of a pair of feature templatesextracted from an imposter pair for each latent fingerprint Fi (firstbiometric image). FIG. 7 illustrates the calculated first score andsecond score for each latent fingerprint Fi. Note that the first scoreand the second score may be calculated for each tenprint fingerprint fj(second biometric image).

Next, the optimum solution determination unit 14 calculates, as a scoreseparation degree S, a ratio of the latent fingerprint Fi (the firstbiometric image) whose first score is ranked top on a temporaryparameter P(A, B, C) basis. Note that a ratio of the tenprintfingerprint fj (the second biometric image) whose first score is rankedtop may be calculated as a score separation degree S on a temporaryparameter P(A, B, C) basis. FIG. 7 illustrates the rank of the firstscore of the latent fingerprint Fi in parentheses.

Finally, the optimum solution determination unit 14 determines thetemporary parameter P(A, B, C) having the maximum score separationdegree S as the optimum solution. For example, in the exampleillustrated in FIG. 7 , the temporary parameter P(1, 2, 1) having themaximum score separation degree S(=3/3) is determined as the optimumsolution.

The parameter update unit 15 then updates the parameter stored in thestorage unit 2 with the temporary parameter determined by the optimumsolution determination unit 14 as the optimum solution. Thereby, theparameter stored in the storage unit 2 is optimized, which enablesautomatic optimization of the parameter used for extracting a featuretemplate from a biometric image while operating the biometricrecognition apparatus.

In particular, in the present example embodiment, the score separationdegree S is calculated based on the first score representing asimilarity degree of a pair of feature templates extracted from agenuine pair and the second score representing a similarity degree of apair of feature templates extracted from an imposter pair. It istherefore possible to avoid determining, as the optimum solution, atemporary parameter in which the second score representing a similaritydegree of a pair of feature templates extracted from an imposter pair,such as the temporary parameter (2, 1, 1) illustrated in FIG. 7 , iscalculated to be high. Note that the calculation method of the scoreseparation degree is not limited to the determination using a ratio ofthe ranked top.

FIG. 8 is a diagram illustrating a flowchart of a biometric recognitionmethod according to the first example embodiment. The biometricrecognition method of the present example embodiment will be describedbelow with reference to the flowchart illustrated in FIG. 8 .

In step S101, the biometric image read unit 11 reads a biometric imageregistered in the storage unit as a database. In step S102, the teacherdata generation unit 12 selects, out of a plurality of biometric imagesread from the biometric image read unit 11, a pair of biometric imagesdetermined as being based on the same finger as a genuine pair. In stepS103, the learning data generation unit 13 uses a plurality of differenttemporary parameters from the first biometric image and the secondbiometric image used for teacher data generated by the teacher datageneration unit 12 and generates a feature template used for learningdata.

In step S104, the optimum solution determination unit 14 calculates ascore separation degree on a temporary parameter basis based on thefirst score representing a similarity degree of a pair of featuretemplates extracted from a genuine pair and the second scorerepresenting a similarity degree of a pair of feature template extractedfrom an imposter pair. In step S105, the optimum solution determinationunit 14 determines, as the optimum solution, a temporary parameterhaving the maximum score separation degree. In step S106, the parameterupdate unit 15 updates the parameter stored in the storage unit 2 withthe temporary parameter determined as the optimum solution.

As described above, the biometric recognition apparatus of the presentexample embodiment calculates a score separation degree on a temporaryparameter basis based on the first score representing a similaritydegree of a pair of feature templates extracted from a genuine pair andthe second score representing a similarity degree of a pair of featuretemplate extracted from an imposter pair and then determines thetemporary parameter in accordance with the level of the score separationdegree. According to such a configuration, a biometric recognitionapparatus and a fingerprint feature extraction method that canautomatically optimize parameters used for extracting a feature templatefrom a biometric image can be provided.

Other Example Embodiments

Note that each example embodiment described above is a mere embodiedexample in implementing the present invention, and the technical scopeof the present invention should not be construed in a limiting sense bythe example embodiments. That is, the present invention can beimplemented in various forms without departing from the technicalconcept thereof or the primary feature thereof.

For example, the score of the genuine pair F2-f2 in the exampleembodiment described above is calculated to be high regardless of thevalue of the temporary parameter P (A, B, C) used for learning and thushardly contributes to determination of the superiority or inferiority ofa temporary parameter. Accordingly, the optimum solution determinationunit 14 may remove a genuine pair whose score is greater than or equalto a predetermined value (for example, 1500) from the calculationprocess of a score separation degree. Thereby, when there are manygenuine pairs having a large score, a processing load of the controlcalculation unit 1 in the calculation process of the score separationdegree can be significantly reduced.

The present invention can also be realized by a process to supply aprogram implementing one or more functions of the example embodimentdescribed above to a system or an apparatus via a network or a storagemedium and read and execute the program by using one or more processorsin a computer of the system or the apparatus. Further, the presentinvention can be realized by using a circuit that implements one or morefunctions (for example an ASIC).

The whole or part of the example embodiments disclosed above can bedescribed as, but not limited to, the following supplementary notes.

Supplementary Note 1

A biometric recognition apparatus comprising:

-   -   a teacher data generation unit that generates a genuine pair and        an imposter pair of a first biometric image and a second        biometric image;    -   a learning data generation unit that uses a plurality of        different temporary parameters to extract feature templates from        the first biometric image and the second biometric image; and    -   an optimum solution determination unit that calculates a score        separation degree on the temporary parameter basis based on a        first score representing a similarity degree of a pair of the        feature mounts extracted from the genuine pair and a second        score representing a similarity degree of a pair of the feature        templates extracted from the imposter pair and determines the        temporary parameter based on a level of the score separation        degree.

Supplementary note 2

The biometric recognition apparatus according to supplementary note 1,wherein the optimum solution determination unit determines the temporaryparameter based on the score separation degree which is the largest.

Supplementary note 3

The biometric recognition apparatus according to supplementary note 1 or2, wherein the optimum solution determination unit calculates the firstscore and the second score for each of the first biometric image or thesecond biometric image, and calculates, as the score separation degree,a ratio of the first score ranked top.

Supplementary note 4

The biometric recognition apparatus according to any one ofsupplementary notes 1 to 3, wherein the optimum solution determinationunit removes the genuine pair having the first score greater than orequal to a predetermined value from a calculation process of the scoreseparation degree.

Supplementary note 5

The biometric recognition apparatus according to any one ofsupplementary notes 1 to 4 further comprising:

-   -   a storage unit that stores a parameter used for extracting the        feature templates from the first biometric image and the second        biometric image; and    -   a parameter update unit that updates the parameter stored in the        storage unit with the determined temporary parameter.

Supplementary note 6

The biometric recognition apparatus according to any one ofsupplementary notes 1 to 5,

-   -   wherein a pair of the first biometric image and the second        biometric image is a pair of collected images of a fingerprint        or a palm print, and    -   wherein the feature templates form a feature point group of a        fingerprint or a palm print.

Supplementary note 7

The biometric recognition apparatus according to supplementary note 6,wherein a pair of the first biometric image and the second biometricimage is a pair of a collected image of a latent fingerprint and acollected image of a tenprint fingerprint.

Supplementary note 8

The biometric recognition apparatus according to any one ofsupplementary notes 1 to 7, wherein the temporary parameter includes acombination of a noise reduction process and an enhancement process anda weight.

Supplementary note 9

A biometric recognition method used in a control calculation unit of abiometric recognition apparatus, the biometric recognition methodcomprising:

-   -   a teacher data generation step of generating a genuine pair and        an imposter pair of a first biometric image and a second        biometric image;    -   a learning data generation step of using a plurality of        different temporary parameters to extract feature templates from        the first biometric image and the second biometric image; and    -   an optimum solution determination step of calculating a score        separation degree on the temporary parameter basis based on a        first score representing a similarity degree of a pair of the        feature mounts extracted from the genuine pair and a second        score representing a similarity degree of a pair of the feature        templates extracted from the imposter pair and determining the        temporary parameter based on a level of the score separation        degree.

Supplementary note 10

A computer readable storage medium storing a program that causes acomputer to function as:

-   -   a teacher data generation unit configured to generate a genuine        pair and an imposter pair of a first biometric image and a        second biometric image;    -   a learning data generation unit configured to use a plurality of        different temporary parameters to extract feature templates from        the first biometric image and the second biometric image; and    -   an optimum solution determination unit configured to calculate a        score separation degree on the temporary parameter basis based        on a first score representing a similarity degree of a pair of        the feature templates extracted from the genuine pair and a        second score representing a similarity degree of a pair of the        feature templates extracted from the imposter pair and determine        the temporary parameter based on a level of the score separation        degree.

While the present invention has been described above with reference tothe example embodiments, the present invention is not limited to theexample embodiments described above. Various changes that can beappreciated by those skilled in the art within the scope of the presentinvention may be applied to the configuration or the detail of thepresent invention.

This application is based upon and claims the benefit of priority fromJapanese Patent Application No. 2017-199167, filed on Oct. 13, 2017, thedisclosure of which is incorporated herein in its entirety by reference.

REFERENCE SIGNS LIST

-   1 control calculation unit-   2 storage unit-   11 biometric image read unit-   12 teacher data generation unit-   13 learning data generation unit-   14 optimum solution determination unit-   15 parameter update unit

The invention claimed is:
 1. A biometric recognition apparatuscomprising: one or more processors; a memory storing instructionsexecutable by the one or more processors to: generate, from a pluralityof first biometric images and a plurality of second biometric images, aplurality of pairs that are each a combination of a respective one ofthe first biometric images and a respective one of the second biometricimages, the plurality of pairs including a genuine pair and an imposterpair; use a plurality of different temporary parameters to extractfeature templates from the first biometric image and the secondbiometric image of each pair; calculate a similarity degree of thefeature templates extracted from the first biometric image and thesecond biometric image of each pair; calculate a first scorerepresenting the similarity degree of the feature templates extractedfrom the first biometric image of the genuine pair; in response to thefirst score being greater than or equal to a predetermined value,replace the genuine pair with a new genuine pair and recalculate thefirst score; calculate a second score representing the similarity degreeof the feature templates extracted from the first biometric image andthe second biometric image of the imposter pair; calculate a scoreseparation degree on a temporary parameter basis based on the firstscore and the score second; and determine the temporary parameter basedon a level of the score separation degree.
 2. The biometric recognitionapparatus according to claim 1, wherein the temporary parameter isdetermined based on the score separation degree that is largest.
 3. Thebiometric recognition apparatus according to claim 1, wherein the firstscore is calculated for each first biometric image and the second scoreis calculated for each of the second biometric image, and the scoreseparation degree is calculated a ratio of the first score that istop-ranked.
 4. The biometric recognition apparatus according to claim 1,wherein the instructions are executable by the one or more processors tofurther: store a parameter used for extracting the feature templatesfrom the first biometric image and the second biometric image of eachpair; and update the parameter with the determined temporary parameter.5. The biometric recognition apparatus according to claim 1, whereineach pair is a pair of collected images of a fingerprint or a palmprint, and wherein the feature templates form a feature point group ofthe fingerprint or the palm print.
 6. The biometric recognitionapparatus according to claim 5, wherein each pair is a pair of acollected image of a latent fingerprint and a collected image of atenprint fingerprint.
 7. The biometric recognition apparatus accordingto claim 1, wherein the temporary parameter includes a combination of anoise reduction process and an enhancement process and a weight.
 8. Abiometric recognition method comprising: generating, by a processor andfrom a plurality of first biometric images and a plurality of secondbiometric images, a plurality of pairs that are each a combination of arespective one of the first biometric images and a respective one of thesecond biometric images, the plurality of pairs including a genuine pairand an imposter pair; using, by the processor, a plurality of differenttemporary parameters to extract feature templates from the firstbiometric image and the second biometric image of each pair;calculating, by the processor, a similarity degree of the featuretemplates extracted from the first biometric image and the secondbiometric image of each pair; calculating, by the processor, a firstscore representing the similarity degree of the feature templatesextracted from the first biometric image of the genuine pair; inresponse to the first score being greater than or equal to apredetermined value, replacing, by the processor, the genuine pair witha new genuine pair and recalculate the first score; calculating, by theprocessor, a second score representing the similarity degree of thefeature templates extracted from the first biometric image and thesecond biometric image of the imposter pair; calculating, by theprocessor, a score separation degree on a temporary parameter basisbased on the first score and the score second; and determining, by theprocessor, the temporary parameter based on a level of the scoreseparation degree.
 9. A computer readable non-transitory storage mediumstoring a program that is executable by a computer to cause the computerto perform: generating, from a plurality of first biometric images and aplurality of second biometric images, a plurality of pairs that are eacha combination of a respective one of the first biometric images and arespective one of the second biometric images, the plurality of pairsincluding a genuine pair and an imposter pair; using a plurality ofdifferent temporary parameters to extract feature templates from thefirst biometric image and the second biometric image of each pair;calculating a similarity degree of the feature templates extracted fromthe first biometric image and the second biometric image of each pair;calculating a first score representing the similarity degree of thefeature templates extracted from the first biometric image of thegenuine pair; in response to the first score being greater than or equalto a predetermined value, replacing the genuine pair with a new genuinepair and recalculate the first score; calculating a second scorerepresenting the similarity degree of the feature templates extractedfrom the first biometric image and the second biometric image of theimposter pair; calculating a score separation degree on a temporaryparameter basis based on the first score and the score second; anddetermining the temporary parameter based on a level of the scoreseparation degree.